1. Field of the Invention
The present invention relates to a method for delivering data packets, and, more particularly, to a method for wirelessly delivering data packets.
2. Description of the Related Art
Assembly line manufacturing systems and process control plants often include control loops having several hundred sensors that continually monitor the system performance and provide feedback to a programmable logic controller (PLC). The PLC then, based on the feedback, induces the necessary control actions for controlling the systems. Most existing current day systems employ wired solutions where sensors are connected to the PLC via wires or bus systems.
The use of wireless sensors instead of wired sensors in the control loops can provide several advantages. For instance, the use of wireless sensors may significantly simplify the mechanical design of these machines and lead to more compact designs. Another advantage is that problems caused by wear and tear of cables can be eliminated, thereby leading to reduced maintenance down-time and increased production throughput. Yet another advantage of wireless sensors is that they may provide for easier installation and maintenance. A further advantage of wireless sensors is that they may bring about a reduction in unit cost due to elimination of wire/bus systems and the required associated accessories.
Discrete event control loops in modern day machines often comprise a large number of sensors (e.g., between 50 and 200) reporting to a controller. Many discrete control applications must cater to hard real-time requirements. For example, sensors must communicate the occurrence of critical events to the controller within a real-time deadline (usually between 5 and 50 milliseconds after the occurrence) specified by the control system's design requirements. Messages received after this deadline are considered lost. In the event of traffic bursts wherein several sensors may attempt to communicate with the programmable logic controller (PLC) at the same time, messages from all the sensors must reach within the specified deadline. Thus, the metric for performance in such systems is the probability that a message from all the sensors succeeds in being received at the controller within this deadline. For such solutions to be viable, the sensors must last for several years without requiring change of batteries.
The operation of several modern day control systems such as computer numerically controlled (CNC) machines, vehicles, manufacturing robot arrays, etc., are based on discrete event control. In a discrete event control system, sensors convey the occurrence of critical events (rather than sampled values of continuous physical phenomena) to a controller. The controller then, based on these sensory inputs from the sensors, induces the necessary actuation to control the system. For example, proximity sensors at a welding unit may detect and notify the arrival of a new work piece to the controller. The controller may then induce a robotic arm to pick the piece up and place it on the welding platform. In another example, some sensors may detect a possible oil/gas leak and, upon notification, the controller may require shutting down some sections of the system.
The typical modern day discrete event control based CNC machine or vehicle spans between three and fifteen meters along its largest dimension (controller to sensors) and houses between 50 and 200 sensors. For a large number of discrete event control based systems, the control loop must cater to hard real-time requirements. For example, the sensing (detection of the event), communication (sensor to controller and controller to actuator) and actuation must occur within a pre-specified deadline. Given fixed sensing and actuation delays, such deadlines can usually be translated into communication delay deadlines. A message that does not reach its destination before this deadline may cause the machine to go into an error condition that requires its temporary halting or resetting. In machines today, sensors and actuators communicate to the controller via cables and cater to hard real-time communication latencies ranging from five to fifty milliseconds depending on the specifics of the machine.
Inherent to most discrete event control systems is the unpredictable, volatile and “bursty” nature of the traffic. That is, it is difficult to predict when, how many, or which sensors will be triggered to communicate at the same time to the controller. This is because the communication is primarily event driven, and it is often impossible to predict the times and nature of occurrence of external events. In general, traffic bursts are common in most discrete event systems because i) a single event may lead to several sensors triggering at the same time, and ii) more than one event may occur at the same time or very close in time. Such event driven bursts may be referred to herein as “sensor bursts.” In the event of a sensor burst, messages from all the sensors must reach the controller within the specified deadline since the controller can take appropriate action only upon receiving all the inputs. Failure of receipt of a message from even one sensor may lead to unpredictable failures in the system, forcing it into an error recovery state.
Consider a production machine of 100 sensors that produces a finished product every ten seconds. Suppose that the manufacturing of each product triggers an average of about 50 sensor burst events. A communication failure probability of one in a million translates into an expected time between failures of about two days (106/(86,400×50/10). A failure will lead to a decreased production throughput and hence significant financial losses. Thus, probability of failure (or, in some cases, expected time between failures) is perhaps the most important performance measure in most production systems. Failures in machines such as vehicles may lead to graver consequences including loss of life and property. Not surprisingly, in modern day machines, sensor-actuator-controller communication is conducted via communication cables, such as buses or wires carrying analog signals.
The design of present day machines requires careful deliberation for routing the cables from various locations within the machine to the controller. Eliminating the cables can not only provide the potential of enabling compact and simple mechanical designs by avoiding cumbersome cabling, but can also provide additional benefits in terms of ease of installation and maintenance. Furthermore, cables are often subject to wear and tear, especially when they are drawn from moving parts within the machine and require frequent maintenance. Each maintenance cycle translates to decreased usage and increased maintenance costs. Elimination of cable wear and tear events translate into lesser maintenance expenditure and fewer down periods. Further, the possibility of wireless sensors encourages the design of systems with a larger number of sensing points for more efficient control.
For most machines, actuators have very high power requirements. The actuators are usually expected to induce mechanical operations such as lifting a part or turning a high speed drill. Thus, actuators cannot be untethered, and require power cables to be routed to them. Making the controller-actuator communication wireless does not offer significant advantages since controller-actuator communication cables can be “bundled” up along with the power lines without significant overhead. Sensors, on the other hand, have modest power requirements, and can be made “completely wireless”, operating only on batteries. Replacing batteries in hundreds of sensors in a machine, however, can be a time-consuming, labor-intensive job. Frequent battery changes resulting in maintenance downtime can offset the gains offered by a wireless sensing system. Considering that the lifetime of typical manufacturing machines or vehicles is ten years, it will be expected that wireless sensors operate for at least a few years on batteries before requiring battery replacement.
There are thus two metrics that completely capture the performance of a communication protocol for low-latency, hard real-time discrete event systems. The first metric is the probability of communication error, which may be defined as the probability that at least one sensor (among all sensors attempting to communicate in the event of a sensor burst) does not succeed in transmitting its message to the controller within the deadline. The second metric is the longevity of the system, which may be defined as the average life expectancy of wireless sensor nodes.
Existing MAC mechanisms include S-MAC, which relies on locally (topologically) synchronizing the sleep and wake up schedules of sensor nodes to increase network longevity; TMAC, which enhances S-MAC's performance under variable load traffic; DSMAC, which allows for an adaptive duty-cycling window to cater to delay-sensitive applications; WiseMAC, which uses spatial TDMA and np-CSMA, where nodes sample the channel periodically to sniff the channel based on wake up schedules that are offset to avoid collisions; TRAMA, which relies on spatial TDMA but includes an additional mechanism to avoid energy wastage due to the problem of hidden terminals not addressed in WiseMAC; DMAC, which is a MAC designed specifically for networks where sensors form a tree topology to transmit data to a base-station, and which provides both low-latencies and longevity by ensuring skewed schedules among various levels in the trees which enable children nodes to transmit exactly when parent nodes are ready to listen; SIFT, which is a contention-based MAC that uses a non-uniform probability distribution to pick transmission slots and exponentially adapts the transmission probabilities on noticing idle slots; PTDMA, which attempts to seamlessly transition between TDMA and CSMA by assigning probabilistic ownerships to slots that are adjusted based on the number of transmitters; and ZMAC, which transitions between TDMA and CSMA seamlessly but is suitable for multihop networks, unlike PTDMA which was designed with single hop networks in mind.
Communication cable-based solutions used in modern day machines typically provide error rates of one in a million or less. A wireless system that replaces an existing system only to become a performance bottleneck is not acceptable as a viable solution.